Our group is dedicated to developing machine learning algorithms to leverage large medical data stored in Electronic Health Records. Our approach brings to bear new methods to derive accurate, multi-dimensional models from large collections of observational data.

Our research team brings together machine learning, natural language processing experts, database architects and programmers from the Center for Computational Learning Systems (CCLS) at Columbia University along with clinicians from Columbia University Medical Center (CUMC).

Develop algorithms in the SVM family that allow extra information to be used effectively during training, with the understanding that this extra information will not be available during actual operation

Description:
Learning extra information like structural homologies between proteins in a system designed to predict structure from amino acid sequences

Description:
The project aims to estimate the time between (and to) failures of primary distribution feeders and their components (such as sections and joints).

In the New York City Power Grid, electricity is transmitted via primary distribution feeders between the high voltage transmission system and the household-voltage secondary system. These feeders are susceptible to different kinds of failures such as emergency isolation caused by automatic substation relays (Open Autos), failing on test, maintainence crew noticing problems and scheduled work on different sections of the feeder.